1. Field of the Invention
The present invention relates to the field of image recognition and, more particularly, to a method for extracting and matching gesture features of image by performing a curvature scale space (CSS) image operation.
2. Description of Related Art
In the field of gesture recognition, the vision-based static gesture recognition is made possible by recognizing posture or shape of a gesture image. Hence, techniques about extracting and matching gesture features (e.g., posture or shape) of image are critical with respect to image recognition.
Conventionally, two modes of description are employed to recognize gesture features of image. One is characterized by utilizing gesture region description to describe a region occupied by an object. Shape moment descriptors are utilized to calculate the shape moments at various stages for obtaining a quantitative shape feature description which is in turn used as description parameters irrespective of size, rotation angle, and movement of an object. For example, Zernike moment (ZM) or pseudo-Zernike moment (PZM) is used as shape feature vector. The other one is known as a gesture shape boundary description for describing a real contour of image. It is characterized by converting a contour coordinate into a frequency region by Fourier descriptors so as to extract Fourier coefficients as features. It also provides description parameters which are irrespective of size, rotation angle, and movement of an object. Alternatively, an orientation histogram is utilized to obtain all edge points of a contour in order to obtain an orientation as features by calculation.
However, the above conventional techniques are not reliable due to noise interference. For example, in the case of utilizing gesture region description to describe a region, the shape moment descriptors are suitable for gesture shape description while its result is not reliable due to noise interference in searching a shape centroid or shape center. Furthermore, in the case of gesture shape boundary description, although Fourier descriptors is suitable for the description of gesture contour, it is also susceptible to noise interference, and its result is related to the number of edge points of contour to be processed. Moreover, the orientation histogram utilized to obtain all edge points of a contour is sensitive to the rotation angle. Therefore, it is desirable to provide a novel method for extracting and matching gesture features of image to mitigate and/or obviate the aforementioned problems.